Search results for: artificial intelligence in medicine

Authors: Sakshi V. Tantak, Vishap K. Malik, Neelanjney Pilarisetty

Abstract:

A patent is an exclusive right granted for an invention. It can be a product or a process that provides an innovative method of doing something, or offers a new technical perspective or solution to a problem. A patent can be obtained by making the technical information and details about the invention publicly available. The patent owner has exclusive rights to prevent or stop anyone from using the patented invention for commercial uses. Any commercial usage, distribution, import or export of a patented invention or product requires the patent owner’s consent. It has been observed that the central and important parts of patents are scripted in idiosyncratic and complex linguistic structures that can be difficult to read, comprehend or interpret for the masses. The abstracts of these patents tend to obfuscate the precise nature of the patent instead of clarifying it via direct and simple linguistic constructs. This makes it necessary to have an efficient access to this knowledge via concise and transparent summaries. However, as mentioned above, due to complex and repetitive linguistic constructs and extremely long sentences, common extraction-oriented automatic text summarization methods should not be expected to show a remarkable performance when applied to patent documents. Other, more content-oriented or abstractive summarization techniques are able to perform much better and generate more concise summaries. This paper proposes an efficient summarization system for patents using artificial intelligence, natural language processing and deep learning techniques to condense the knowledge and essential information from a patent document into a single summary that is easier to understand without any redundant formatting and difficult jargon.

Keywords: abstractive summarization, deep learning, natural language Processing, patent document

Procedia PDF Downloads 120

Authors: Ren Moses

Abstract:

The process of classifying vehicles on a highway is hereby viewed as a pattern recognition problem in which connectionist techniques such as artificial neural networks (ANN) can be used to assign vehicles to their correct classes and hence to establish optimum axle spacing thresholds. In the United States, vehicles are typically classified into 13 classes using a methodology commonly referred to as “Scheme F”. In this research, the ANN model was developed, trained, and applied to field data of vehicles. The data comprised of three vehicular features—axle spacing, number of axles per vehicle, and overall vehicle weight. The ANN reduced the classification error rate from 9.5 percent to 6.2 percent when compared to an existing classification algorithm that is not ANN-based and which uses two vehicular features for classification, that is, axle spacing and number of axles. The inclusion of overall vehicle weight as a third classification variable further reduced the error rate from 6.2 percent to only 3.0 percent. The promising results from the neural networks were used to set up new thresholds that reduce classification error rate.

Keywords: artificial neural networks, vehicle classification, traffic flow, traffic analysis, and highway opera-tions

Procedia PDF Downloads 309

Authors: Syed Asif Hassan, Tabrej Khan, Ibrahim Manssor Al Najar, Mohammed Nasser

Abstract:

The incredible accomplishment achieved by e-commerce in consumer durable area encouraged us to implement the online e-commerce model to tap the business benefits of electronic pharmacy in Saudi Arabia. The Kingdom of Saudi Arabia is famous for traditional herbal medicine. The rich heritage of traditional medicine has helped the mushrooming of regional pharmaceutical industries manufacturing drugs and other therapeutic against various diseases. However, the implementation of e-commerce in pharmacy has not been employed in the Kingdom of Saudi Arabia. The electronic pharmacy (E-Pharm) is an important sector that is flourishing across the globe and providing benefits of E-Pharm to the customers and suppliers all around the world. In this context, our web-based application of electronic pharmacy is the one of its kind in the Kingdom of Saudi Arabia. Surveys and personal interviews were used to identify key objectives of the proposed web-based portal. As per the findings of the surveys and personal interviews, following key objectives were identified: (a) The online platform will be used for ordering of prescription based medications for consumers. (b) The e-portal will provide space for pharmaceutical retailers who do not have an electronic platform to upload and sell their therapeutic products in an organized way. (c) The web portal will provide a tracking system to track the customer’s behavior like choice, offer, order, shipment, payment, etc. The web-based e-pharmacy portal will be developed using MySQL and PHP. The development of e-pharmacy web portal and e-prescription practices will not only improve the growth of electronic pharmacy but would also decrease the possibility of prescription alteration thus providing safety and improving the quality of service provided to the patient or consumers.

Keywords: e-commerce, E-Pharm, MySQL, PHP

Procedia PDF Downloads 396

Authors: Adrian Ionita, Ana-Maria Ghimes

Abstract:

The lack of features, design and the lack of promoting an integrated booking application are some of the reasons why most online travel platforms only offer automation of old booking processes, being limited to the integration of a smaller number of services without addressing the user experience. This paper represents a practical study on how to improve travel applications creating user-profiles through data-mining based on neural networks and genetic algorithms. Choices made by users and their ‘friends’ in the ‘social’ network context can be considered input data for a recommendation engine. The purpose of using these algorithms and this design is to improve user experience and to deliver more features to the users. The paper aims to highlight a broader range of improvements that could be applied to travel applications in terms of design and service integration, while the main scientific approach remains the technical implementation of the neural network solution. The motivation of the technologies used is also related to the initiative of some online booking providers that have made the fact that they use some ‘neural network’ related designs public. These companies use similar Big-Data technologies to provide recommendations for hotels, restaurants, and cinemas with a neural network based recommendation engine for building a user ‘DNA profile’. This implementation of the ‘profile’ a collection of neural networks trained from previous user choices, can improve the usability and design of any type of application.

Keywords: artificial intelligence, big data, cloud computing, DNA profile, genetic algorithms, machine learning, neural networks, optimization, recommendation system, user profiling

Procedia PDF Downloads 162

Authors: Debarati Bhaumik, Diptish Dey

Abstract:

Multilevel models, increasingly deployed in industries such as insurance, food production, and entertainment within functions such as marketing and supply chain management, need to be transparent and ethical. Applications usually result in binary classification within groups or hierarchies based on a set of input features. Using open-source datasets, we demonstrate that popular explainability methods, such as SHAP and LIME, consistently underperform inaccuracy when interpreting these models. They fail to predict the order of feature importance, the magnitudes, and occasionally even the nature of the feature contribution (negative versus positive contribution to the outcome). Besides accuracy, the computational intractability of SHAP for binomial classification is a cause of concern. For transparent and ethical applications of these hierarchical statistical models, sound audit frameworks need to be developed. In this paper, we propose an audit framework for technical assessment of multilevel regression models focusing on three aspects: (i) model assumptions & statistical properties, (ii) model transparency using different explainability methods, and (iii) discrimination assessment. To this end, we undertake a quantitative approach and compare intrinsic model methods with SHAP and LIME. The framework comprises a shortlist of KPIs, such as PoCE (Percentage of Correct Explanations) and MDG (Mean Discriminatory Gap) per feature, for each of these three aspects. A traffic light risk assessment method is furthermore coupled to these KPIs. The audit framework will assist regulatory bodies in performing conformity assessments of AI systems using multilevel binomial classification models at businesses. It will also benefit businesses deploying multilevel models to be future-proof and aligned with the European Commission’s proposed Regulation on Artificial Intelligence.

Keywords: audit, multilevel model, model transparency, model explainability, discrimination, ethics

Procedia PDF Downloads 90

Authors: N. D. Iswarya, M. A. Maluk Mohamed, N. Vijaya

Abstract:

Grid is infrastructure that allows the deployment of distributed data in large size from multiple locations to reach a common goal. Scheduling data intensive applications becomes challenging as the size of data sets are very huge in size. Only two solutions exist in order to tackle this challenging issue. First, computation which requires huge data sets to be processed can be transferred to the data site. Second, the required data sets can be transferred to the computation site. In the former scenario, the computation cannot be transferred since the servers are storage/data servers with little or no computational capability. Hence, the second scenario can be considered for further exploration. During scheduling, transferring huge data sets from one site to another site requires more network bandwidth. In order to mitigate this issue, this work focuses on incorporating cognitive science in scheduling. Cognitive Science is the study of human brain and its related activities. Current researches are mainly focused on to incorporate cognitive science in various computational modeling techniques. In this work, the problem solving approach of human brain is studied and incorporated during the data intensive scheduling in grid environments. Here, a cognitive engine is designed and deployed in various grid sites. The intelligent agents present in CE will help in analyzing the request and creating the knowledge base. Depending upon the link capacity, decision will be taken whether to transfer data sets or to partition the data sets. Prediction of next request is made by the agents to serve the requesting site with data sets in advance. This will reduce the data availability time and data transfer time. Replica catalog and Meta data catalog created by the agents assist in decision making process.

Keywords: data grid, grid workflow scheduling, cognitive artificial intelligence

Procedia PDF Downloads 392

Authors: Veronika Lesáková, Silvia Slezáková, František Štěpánek

Abstract:

Additive manufacturing technologies producing drug dosage forms aimed at personalized medicine applications are promising strategies with several advantages over the conventional production methods. One of the emerging technologies is 3D printing which reduces manufacturing steps and thus allows a significant drop in expenses. A decrease in material consumption is also a highly impactful benefit as the tested drugs are frequently expensive substances. In addition, 3D printed dosage forms enable increased patient compliance and prevent misdosing as the dosage forms are carefully designed according to the patient’s needs. The incorporation of multiple drugs into a single dosage form further increases the degree of personalization. Our research focuses on the development of 3D printed tablets incorporating multiple drugs (candesartan, losartan) and thermoplastic polymers (e.g., KlucelTM HPC EF). The filaments, an essential feed material for 3D printing,wereproduced via hot-melt extrusion. Subsequently, the extruded filaments of various formulations were 3D printed into tablets using an FDM 3D printer. Then, we have assessed the influence of the internal structure of 3D printed tablets and formulation on dissolution behaviour by obtaining the dissolution profiles of drugs present in the 3D printed tablets. In conclusion, we have developed tablets containing multiple drugs providing modified release profiles. The 3D printing experiments demonstrate the high tunability of 3D printing as each tablet compartment is constructed with a different formulation. Overall, the results suggest that the 3D printing technology is a promising manufacturing approach to dual tablet preparation for personalized medicine.

Keywords: 3D printing, drug delivery, hot-melt extrusion, dissolution kinetics

Procedia PDF Downloads 166

Authors: A. Wahid Uddin

Abstract:

Osmotic dilator for cervical ripening bypasses the initial hormonal exposure necessary for a routine method of induction. The study was a clinical intervention with an osmotic dilator followed by prospective observation. The aim was to calculate the percentage of women who had successful cervical ripening using modified BISHOP score as evidenced by artificial rupture of membrane. The study also estimated the delivery interval following a single administration of osmotic dilators. Randomly selected patients booked for induction of labour accepting the intervention were included in the study. The study population comprised singleton term pregnancy, cephalic presentation, intact membranes with a modified BISHOP score of less than 6. Initial sample recruited was 30, but 6 patients left the study and the study was concluded on 24 patients. The data were collected in a pre-designed questionnaire and analysis were expressed in percentages along with using mean value for continuous variables. In 70 % of cases, artificial rupture of the membrane was possible and the mean time from insertion of the osmotic dilator to the delivery interval was 30 hours. The study concluded that an osmotic dilator could be a suitable alternative for hormone-based induction of labour.

Keywords: dilator, induction, labour, osmotic

Procedia PDF Downloads 137

Authors: Aschalew Cherie Workneh, K. S. Hari Prasad, Chandra Shekhar Prasad Ojha

Abstract:

Due to the increase in water scarcity, the crop water stress index (CWSI) is receiving significant attention these days, especially in arid and semiarid regions, for quantifying water stress and effective irrigation scheduling. Nowadays, machine learning techniques such as neural networks are being widely used to determine CWSI. In the present study, the performance of two artificial neural networks, namely, Self-Organizing Maps (SOM) and Feed Forward-Back Propagation Artificial Neural Networks (FF-BP-ANN), are compared while determining the CWSI of rice crop. Irrigation field experiments with varying degrees of irrigation were conducted at the irrigation field laboratory of the Indian Institute of Technology, Roorkee, during the growing season of the rice crop. The CWSI of rice was computed empirically by measuring key meteorological variables (relative humidity, air temperature, wind speed, and canopy temperature) and crop parameters (crop height and root depth). The empirically computed CWSI was compared with SOM and FF-BP-ANN predicted CWSI. The upper and lower CWSI baselines are computed using multiple regression analysis. The regression analysis showed that the lower CWSI baseline for rice is a function of crop height (h), air vapor pressure deficit (AVPD), and wind speed (u), whereas the upper CWSI baseline is a function of crop height (h) and wind speed (u). The performance of SOM and FF-BP-ANN were compared by computing Nash-Sutcliffe efficiency (NSE), index of agreement (d), root mean squared error (RMSE), and coefficient of correlation (R²). It is found that FF-BP-ANN performs better than SOM while predicting the CWSI of rice crops.

Keywords: artificial neural networks; crop water stress index; canopy temperature, prediction capability

Procedia PDF Downloads 116

Authors: Ramiro Quintanilla-Licea, Isvar K. Angeles-Hernandez, Javier Vargas-Villarreal

Abstract:

Amebiasis caused by Entamoeba histolytica, associated with high morbidity and mortality, is currently a significant public health problem worldwide, especially in developing countries. In the world, around 50 million people develop this illness, and up to 100,000 deaths occur annually. Due to the side-effects and the resistance that pathogenic protozoa show against common antiparasitic drugs (e.g., metronidazole), growing attention has been paid to plants used in traditional medicine around the world to find new antiprotozoal agents. In this study is reported about the isolation and structure elucidation of antiamoebic compounds occurring in Lippia graveolens Kunth (Mexican oregano). The work-up of the methanol extract of L. graveolens afforded the known flavonoids pinocembrin (1), sakuranetin (2), cirsimaritin (3) and naringenin (4) by bioguided isolation using several chromatographic techniques. Structural elucidation of the isolated compounds was based on spectroscopic/spectrometric analyses (IR; 1H- and 13C-NMR; MS) and comparison with literature data. These compounds showed significant antiprotozoal activity against Entamoeba histolytica trophozoites using in vitro tests (positive control metronidazole IC50 0.205 µg/mL). The antiprotozoal activity of pinocembrin and naringenin (IC50 of 29.51 µg/mL and 28.85 µg/mL, respectively) was higher compared with sakuranetin (44.47 µg/mL) and with cirsimaritin (150.00 µg/mL), revealing that a 5,7-dihydroxylated A ring is essential for antiprotozoal activity. These research funds may validate the use of this plant in the traditional Mexican medicine for the treatment of some digestive disorders and can help to integrate the use of extracts of L. graveolens in the conventional and complementary medicine for the treatment of parasitic diseases.

Keywords: amoebiasis, antiprotozoal agents, bioguided isolation, infectious diseases

Procedia PDF Downloads 189

Authors: Dongkwon Han, Sangho Kim, Sunil Kwon

Abstract:

Unconventional gas resources have dramatically changed the future energy landscape. Unlike conventional gas resources, the key challenges in unconventional gas have been the requirement that applies to advanced approaches for production forecasting due to uncertainty and complexity of fluid flow. In this study, artificial neural network (ANN) model which integrates machine learning and data-driven approach was developed to predict productivity in shale gas. The database of 129 wells of Eagle Ford shale basin used for testing and training of the ANN model. The Input data related to hydraulic fracturing, well completion and productivity of shale gas were selected and the output data is a cumulative production. The performance of the ANN using all data sets, clustering and variables importance (VI) models were compared in the mean absolute percentage error (MAPE). ANN model using all data sets, clustering, and VI were obtained as 44.22%, 10.08% (cluster 1), 5.26% (cluster 2), 6.35%(cluster 3), and 32.23% (ANN VI), 23.19% (SVM VI), respectively. The results showed that the pre-trained ANN model provides more accurate results than the ANN model using all data sets.

Keywords: unconventional gas, artificial neural network, machine learning, clustering, variables importance

Procedia PDF Downloads 194

Authors: Liya Abraham

Abstract:

Eritrea is located along the Red Sea, north of the Horn of Africa, between Djibouti and Sudan and has a population of about 3.2 million as of 2010. It has six administrative regions; Anseba, Debub, Debubawi K’eyih Bahri, Gash-Barka, Ma'akel, and Semenawi K’eyih Bahri. Eritrea has got its independence in 1991 after 30 years war of liberation. The country is blessed with various medicinal flora and fauna, and marine and terrestrial biodiversity. Traditional Medicine (TM) has been an integral part of the Eritrean culture for centuries. So far, more than 19 TM modalities have been recognized, and are broadly categorized as; herbal, procedure-based and spiritual. Despite the availability of modern medicine to the majority of the population, TM is still widely practiced. The rationale behind widespread use is accessibility, affordability and cultural acceptability. Hence, TM is of great contribution to the Eritrean health care system. As a matter of fact, harnessing the potential contribution of effective and safe TM in order to attain Universal Health Coverage (UHC) has been emphasized in the WHO TM strategy 2014-2023. The Eritrean TM, however, was operating without regulation and reliable scientific justification behind its safety and efficacy. Thus, the Ministry of Health (MoH), in recognition of the role of TM in primary healthcare and safeguard public health, established a regulatory body for TM so-called as Traditional Medicine Unit (TMU) in 2012. The mission of the unit is to ensure rational TM use through an integrated health service delivery system and contribute to the country’s economic and social development. The unit has established its national TM policy in 2017. The activities of the unit are guided by the National TM Advisory Committee (TMAC), responsible for the provision of technical assistance and advisory role. Moreover, the Legal Framework and Code of Ethics and Practice which provide a legal basis for the regulation of TM have also been drafted. In recognition of the importance of TM research and development, the unit launched a nationwide TM survey in 2017 and had surveyed two zones (Gash-Barka and Debub). The findings of the survey were subjected to a research dissemination workshop and publication in international journals. Furthermore, TM-related adverse events reporting tool (Green Form) aiming to guide regulatory interventions and researches have been established by the unit, and ever since reports are flowing. The unit has also been offering training to THPs, pharmacy students and health care professionals regarding TM and its regulatory activities. In addition, as part of the establishment of the national medicinal plants' database and herbal monograph, more than 329 and 30 medicinal plants, have been compiled respectively. In conclusion, TM is still widely accepted and practiced in Eritrea. The TMU ever since its establishment is endeavoring to ensure the safety and efficacy of the TM, and its integration in the mainstream health service delivery system.

Keywords: efficacy, regulation, safety, traditional medicine, traditional medicine unit, universal health coverage

Procedia PDF Downloads 185

Authors: A.Chayaporn Kaoropthai, B. Onjaree Natakuatoong, C. Nagul Cooharojananone

Abstract:

The first year is usually the most critical year for university students. Generally, a considerable number of first-year students worldwide drop out of university every year. One of the major reasons for dropping out is failing. Although they are supposed to have mastered sufficient English proficiency upon completing their high school education, most first-year students are still novices in academic reading. Due to their lack of experience in academic reading, first-year students need significant support from teachers to help develop their academic reading skills. Reading strategies training is thus a necessity and plays a crucial role in classroom instruction. However, individual differences in both students, as well as teachers, are the main factors contributing to the failure in not responding to each individual student’s needs. For this reason, reading strategies training inevitably needs a diagnosis of students’ academic reading skills levels before, during, and after learning, in order to respond to their different needs. To further support reading strategies training, scaffolding is proposed to facilitate students in understanding and practicing using reading strategies under the teachers’ guidance. The use of the Intelligent Tutoring Systems (ITSs) as a tool for diagnosing students’ reading problems will be very beneficial to both students and their teachers. The ITSs consist of four major modules: the Expert module, the Student module, the Diagnostic module, and the User Interface module. The application of Artificial Intelligence (AI) enables the systems to perform diagnosis consistently and appropriately for each individual student. Thus, it is essential to develop the Intelligent Scaffolding Diagnostic Reading Strategies Tutoring Systems to enhance first-year students’ academic reading skills. The systems proposed will contribute to resolving classroom reading strategies training problems, developing students’ academic reading skills, and facilitating teachers.

Keywords: academic reading, intelligent tutoring systems, scaffolding, university students

Procedia PDF Downloads 388

Authors: Prakash Singh

Abstract:

For schools to be desirable places in which to work, it is necessary for principals to recognise their teachers’ emotions, and be sensitive to their needs. This necessitates that principals are capable to correctly identify their emotionally intelligent behaviours (EIBs) they need to use in order to be successful leaders. They also need to have knowledge of their emotional intelligence and be able to identify the factors and situations that evoke emotion at an interpersonal level. If a principal is able to do this, then the control and understanding of emotions and behaviours of oneself and others could improve vastly. This study focuses on the interpersonal EIBS of principals affecting the job satisfaction of teachers. The correlation coefficients in this quantitative study strongly indicate that there is a statistical significance between the respondents’ level of job satisfaction, the rating of their principals’ EIBs and how they believe their principals’ EIBs will affect their sense of job satisfaction. It can be concluded from the data obtained in this study that there is a significant correlation between the sense of job satisfaction of teachers and their principals’ interpersonal EIBs. This means that the more satisfied a teacher is at school, the more appropriate and meaningful a principal’s EIBs will be. Conversely, the more dissatisfied a teacher is at school the less appropriate and less meaningful a principal’s interpersonal EIBs will be. This implies that the leaders’ EIBs can be construed as one of the major factors affecting the job satisfaction of employees.

Keywords: emotional intelligence, teachers' emotions, teachers' job satisfaction, principals' emotionally intelligent behaviours

Procedia PDF Downloads 471

Authors: Ninlawat Phuangchoke, Waraporn Viyanon, Setta Sasananan

Abstract:

The most important process of the water treatment plant process is the coagulation using alum and poly aluminum chloride (PACL), and the value of usage per day is a hundred thousand baht. Therefore, determining the dosage of alum and PACL are the most important factors to be prescribed. Water production is economical and valuable. This research applies an artificial neural network (ANN), which uses the Levenberg–Marquardt algorithm to create a mathematical model (Soft Jar Test) for prediction chemical dose used to coagulation such as alum and PACL, which input data consists of turbidity, pH, alkalinity, conductivity, and, oxygen consumption (OC) of Bangkhen water treatment plant (BKWTP) Metropolitan Waterworks Authority. The data collected from 1 January 2019 to 31 December 2019 cover changing seasons of Thailand. The input data of ANN is divided into three groups training set, test set, and validation set, which the best model performance with a coefficient of determination and mean absolute error of alum are 0.73, 3.18, and PACL is 0.59, 3.21 respectively.

Keywords: soft jar test, jar test, water treatment plant process, artificial neural network

Procedia PDF Downloads 163

Authors: Nadia Masood Khan, Muhammad Salman Khan, Gul Muhammad Khan

Abstract:

Cardiologists perform cardiac auscultation to detect abnormalities in heart sounds. Since accurate auscultation is a crucial first step in screening patients with heart diseases, there is a need to develop computer-aided detection/diagnosis (CAD) systems to assist cardiologists in interpreting heart sounds and provide second opinions. In this paper different algorithms are implemented for automated heart sound classification using unsegmented phonocardiogram (PCG) signals. Support vector machine (SVM), artificial neural network (ANN) and cartesian genetic programming evolved artificial neural network (CGPANN) without the application of any segmentation algorithm has been explored in this study. The signals are first pre-processed to remove any unwanted frequencies. Both time and frequency domain features are then extracted for training the different models. The different algorithms are tested in multiple scenarios and their strengths and weaknesses are discussed. Results indicate that SVM outperforms the rest with an accuracy of 73.64%.

Keywords: pattern recognition, machine learning, computer aided diagnosis, heart sound classification, and feature extraction

Procedia PDF Downloads 260

Authors: Gajaanuja Megalathan, Banuka Athuraliya

Abstract:

Crime is one of our society's most intimidating and threatening challenges. With the majority of the population residing in cities, many experts and data provided by local authorities suggest a rapid increase in the number of crimes committed in these cities in recent years. There has been an increasing graph in the crime rates. People living in Sri Lanka have the right to know the exact crime rates and the crime rates in the future of the place they are living in. Due to the current economic crisis, crime rates have spiked. There have been so many thefts and murders recorded within the last 6-10 months. Although there are many sources to find out, there is no solid way of searching and finding out the safety of the place. Due to all these reasons, there is a need for the public to feel safe when they are introduced to new places. Through this research, the author aims to develop a mobile application that will be a solution to this problem. It is mainly targeted at tourists, and people who recently relocated will gain advantage of this application. Moreover, the Arima Model combined with ANN is to be used to predict crime rates. From the past researchers' works, it is evidently clear that they haven’t used the Arima model combined with Artificial Neural Networks to forecast crimes.

Keywords: arima model, ANN, crime prediction, data analysis

Procedia PDF Downloads 127

Authors: Ikechukwu Ogeze Ukeje, Chinyere Ori Elom, Chukwudum Collins Umoke

Abstract:

This study explores the challenges in the integration of Artificial Intelligence (AI) and Information and Communication Technology (ICT) practices in enhancing governance and sustainable solution modeling in higher education, focusing on Alex Ekwueme Federal University Ndufu-Alike (AE-FUNAI), Nigeria. In the context of a developing country like Nigeria, leveraging AI and ICT tools presents a unique opportunity to improve teaching, learning, administrative processes, and governance. The research aims to evaluate how AI and ICT technologies can contribute to sustainable educational practices, enhance decision-making processes, and improve engagement among key stakeholders: students, lecturers, and administrative staff. Students are involved to provide insights into their interactions with AI and ICT tools, particularly in learning and participation in governance. Lecturers’ perspectives will offer a view into how these technologies influence teaching, research, and curriculum development. Administrative staff will provide a crucial understanding of how AI and ICT tools can streamline operations, support data-driven governance, and enhance institutional efficiency. This study will use a mixed-method approach to collect both qualitative and quantitative data. The finding of this study is geared towards shaping the future of education in Nigeria and beyond by developing an Inclusive AI-governance Integration Framework (I-AIGiF) for enhanced performance in the system. Examining the roles of these stakeholder groups, this research could guide the development of policies for more effective AI and ICT integration, leading to sustainable educational innovation and governance.

Keywords: university administration, AI, higher education governance, education sustainability, ICT challenges

Procedia PDF Downloads 16

Authors: I. Sterbova, E. Oberhofnerova, M. Panek, M. Pavelek

Abstract:

With growing popularity, wood of European larch (Larix decidua, Mill.) is being more often applied into the exterior, usually as facade elements, also without surface treatment. The aim of this work was to compare two laboratory tests of artificial accelerated weathering of wood with two ways of natural weathering in the exterior. To assess changes in selected surface characteristics of larch wood, accelerated weathering methods in the Xenotest and UV chamber were used, both in combination with temperature cycling, for 6 weeks. They were compared with natural weathering results at exposition under 45° and 90° in the exterior for 12 months. The changes of colour, gloss, contact angle of water and also changes in visual characteristics were evaluated. The results of wood surfaces changes after 6 weeks of accelerated weathering in Xenotest are closer to 12 months of natural weathering in the exterior at an angle of 90° compared to the UV chamber testing. The results, especially the colour changes, of the samples exposed at an angle of 45° in the exterior were significantly different. Testing in Xenotest more closely simulates the weathering of façade elements in the exterior compared to the UV chamber testing.

Keywords: larch wood, wooden facade, wood accelerated weathering, weathering methods

Procedia PDF Downloads 138

Authors: Rosa M. Gambier, Jennifer L. Carlson

Abstract:

In order to revitalize the science curriculum for botany courses for non-science majors, we have introduced the use of the medicinal plants into a first-year botany course. We have connected the use of scientific method, scientific inquiry and active learning in the classroom with the study of Western Traditional Medical Botany. The students have researched models of Botanical medicine and have designed a sustainable medicinal plants garden using native medicinal plants from the northeast. Through the semester, the students have researched their chosen species, planted seeds in the college greenhouse, collected germination ratios, growth ratios and have successfully produced a beginners medicinal plant garden. Phase II of the project will be to tie in SCCCs community outreach goals by involving the public in the expanded development of the garden as a way of sharing learning about medicinal plants and traditional medicine outside the classroom.

Keywords: medicinal plant garden, botany curriculum, active learning, community outreach

Procedia PDF Downloads 303

Authors: Victor William Harris

Abstract:

This research is a validation study of the Social Emotional Ability Inventory (SEAI), a multi-dimensional self-report instrument informed by positive psychology, emotional intelligence, social intelligence, and sociocultural learning theory. Designed for use in tandem with the Social Emotional Development (SEAD) theoretical model, the SEAI provides diagnostic-level guidance for professionals and individuals interested in investigating, identifying, and understanding social, emotional strengths, as well as remediating specific social competency deficiencies. The SEAI was shown to be psychometrically sound, exhibited strong internal reliability, and supported the a priori hypotheses of the SEAD. Additionally, confirmatory factor analysis provided evidence of goodness of fit, convergent and divergent validity, and supported a theoretical model that reflected SEAD expectations. The SEAI and SEAD hold potentially far-reaching and important practical implications for theoretical guidance and diagnostic-level measurement of social, emotional competency across a wide range of domains. Strategies researchers, practitioners, educators, and individuals might use to deploy SEAI in order to improve quality of life outcomes are discussed.

Keywords: emotion, emotional ability, positive psychology-social emotional ability, social emotional ability, social emotional ability instrument

Procedia PDF Downloads 254

Authors: Rhaetian Bern D. Azaula

Abstract:

The Philippines' indigenous population, estimated to be 10%-20%, is protected by the Indigenous Peoples Rights Act (IPRA), passed in 1997. However, due to their isolation and limited access to basic services such as health education or needs for health assistance, the law's implementation remains a challenge. As traditional medicine continues to play a significant role in society as the prevention and treatment of some illnesses, it is still customary and widely used to use plants in both traditional and modern ways; however, commercially prepared drugs are progressively advanced as time goes by. Therefore, the purpose of this quantitative study is to investigate the indigenous community at Barangay Magsikap General Nakar, Quezon, and analyze the factors that affect the respondent’s preferences in an indigenous community and reasons for patronizing traditional and commercially prepared medicines and proposes updated health education strategies and instructional materials. Slovin's formula was utilized to reduce the total population representation, followed by stratified sampling for proportional allocation of respondents. The study selects respondents (1) from an Indigenous Community in Barangay Magsikap, General Nakar, Quezon, (2) aged 60 and above, and (3) who are willing to participate. The researcher utilized a checklist-based questionnaire with a Tagalog version, and a Likert Scale was utilized to assess the respondent's choices on selected items. The researcher obtained informed consent from the indigenous community's regional and local office, the chieftain of the tribe, and the respondents, ensuring confidentiality in the collection and retrieval of data. The study revealed that respondents aged 60-69, males with no formal education, are unemployed and have no income source. They prefer traditional medicines due to their affordability, availability, and cultural practices but lack safe preparation, dosages, and contraindications of used medicines. Commercially prepared medications are acknowledged, but respondents are not fully aware of proper administration instructions and dosage labels. Recommendations include disseminating approved herbal medicines and ensuring proper preparation, indications, and contraindications.

Keywords: traditional medicine, commercially prepared medicine, indigenous community, elderly adult

Procedia PDF Downloads 71

Authors: Jinan Fiaidhi, Sabah Mohammed, Petros Zezos

Abstract:

Capsule endoscopy (CE) is an established noninvasive diagnostic modality in investigating small bowel disease. CE has a pivotal role in assessing patients with suspected bleeding or identifying evidence of active Crohn's disease in the small bowel. However, CE produces lengthy videos with at least eighty thousand frames, with a frequency rate of 2 frames per second. Gastroenterologists cannot dedicate 8 to 15 hours to reading the CE video frames to arrive at a diagnosis. This is why the issue of analyzing CE videos based on modern artificial intelligence techniques becomes a necessity. However, machine learning, including deep learning, has failed to report robust results because of the lack of large samples to train its neural nets. In this paper, we are describing a thick data approach that learns from a few anchor images. We are using sound datasets like KVASIR and CrohnIPI to filter candidate frames that include interesting anomalies in any CE video. We are identifying candidate frames based on feature extraction to provide representative measures of the anomaly, like the size of the anomaly and the color contrast compared to the image background, and later feed these features to a decision tree that can classify the candidate frames as having a condition like the Crohn's Disease. Our thick data approach reported accuracy of detecting Crohn's Disease based on the availability of ulcer areas at the candidate frames for KVASIR was 89.9% and for the CrohnIPI was 83.3%. We are continuing our research to fine-tune our approach by adding more thick data methods for enhancing diagnosis accuracy.

Keywords: thick data analytics, capsule endoscopy, Crohn’s disease, siamese neural network, decision tree

Procedia PDF Downloads 155

Authors: Yudhajit Datta

Abstract:

Will is a phenomenon that has puzzled humanity for a long time. It is a belief that Will Power of an individual affects the success achieved by an individual in life. It is thought that a person endowed with great will power can overcome even the most crippling setbacks of life while a person with a weak will cannot make the most of life even the greatest assets. Behavioral aspects of the human experience such as will are rarely subjected to quantitative study owing to the numerous uncontrollable parameters involved. This work is an attempt to subject the phenomena of will to the test of an artificial neural network. The claim being tested is that will power of an individual largely determines success achieved in life. In the study, an attempt is made to incorporate the behavioral phenomenon of will into a computational model using data pertaining to the success of individuals obtained from an experiment. A neural network is to be trained using data based upon part of the model, and subsequently used to make predictions regarding will corresponding to data points of success. If the prediction is in agreement with the model values, the model is to be retained as a candidate. Ultimately, the best-fit model from among the many different candidates is to be selected, and used for studying the correlation between success and will.

Keywords: will power, will, success, apathy factor, random factor, characteristic function, life story

Procedia PDF Downloads 377

Authors: Lígia Pimentel, Miguel Pereira, Manuela Pintado

Abstract:

Wheat germ is a by-product of wheat flour refining. Despite this by-product being a source of proteins, lipids, fibres and complex carbohydrates, and consequently a valuable ingredient to be used in Food Industry, only few applications have been studied. The main goal of this study was to assess the potential prebiotic effect of natural wheat germ. The prebiotic potential was evaluated by in vitro assays with individual microbial strains (Lactobacillus paracasei L26 and Lactobacillus casei L431). A simulated model of the gastrointestinal digestion was also used including the conditions present in the mouth (artificial saliva), oesophagus–stomach (artificial gastric juice), duodenum (artificial intestinal juice) and ileum. The effect of natural wheat germ and wheat germ after digestion on the growth of lactic acid bacteria was studied by growing those microorganisms in de Man, Rogosa and Sharpe (MRS) broth (with 2% wheat germ and 1% wheat germ after digestion) and incubating at 37 ºC for 48 h with stirring. A negative control consisting of MRS broth without glucose was used and the substrate was also compared to a commercial prebiotic fructooligosaccharides (FOS). Samples were taken at 0, 3, 6, 9, 12, 24 and 48 h for bacterial cell counts (CFU/mL) and pH measurement. Results obtained showed that wheat germ has a stimulatory effect on the bacteria tested, presenting similar (or even higher) results to FOS, when comparing to the culture medium without glucose. This was demonstrated by the viable cell counts and also by the decrease on the medium pH. Both L. paracasei L26 and L. casei L431 could use these compounds as a substitute for glucose with an enhancement of growth. In conclusion, we have shown that wheat germ stimulate the growth of probiotic lactic acid bacteria. In order to understand if the composition of gut bacteria is altered and if wheat germ could be used as potential prebiotic, further studies including faecal fermentations should be carried out. Nevertheless, wheat germ seems to have potential to be a valuable compound to be used in Food Industry, mainly in the Bakery Industry.

Keywords: by-products, functional ingredients, prebiotic potential, wheat germ

Procedia PDF Downloads 484

Authors: Amit Verma, Simranjeet Kaur, Sandeep Kaur

Abstract:

Test Case Prioritization (TCP) has gained wide spread acceptance as it often results in good quality software free from defects. Due to the increase in rate of faults in software traditional techniques for prioritization results in increased cost and time. Main challenge in TCP is difficulty in manually validate the priorities of different test cases due to large size of test suites and no more emphasis are made to make the TCP process automate. The objective of this paper is to detect the priorities of different test cases using an artificial neural network which helps to predict the correct priorities with the help of back propagation algorithm. In our proposed work one such method is implemented in which priorities are assigned to different test cases based on their frequency. After assigning the priorities ANN predicts whether correct priority is assigned to every test case or not otherwise it generates the interrupt when wrong priority is assigned. In order to classify the different priority test cases classifiers are used. Proposed algorithm is very effective as it reduces the complexity with robust efficiency and makes the process automated to prioritize the test cases.

Keywords: test case prioritization, classification, artificial neural networks, TF-IDF

Procedia PDF Downloads 393

Authors: Agung A. Ginanjar, Lilitasari, Indra Prasetya, Rizal R. Hanif, Yusrina Rismandini, Atina Hussaana, Nurita P. Sari

Abstract:

Hyperlipidemia is an increase of lipids and cholesterol in the blood that causes the formation of atherosklerosis. The recent pharmacological therapy nowadays is statin. Many Indonesian people use of medicinal plants. There are several medical plants that people always use to cure hyperlipidemia such as bulbs onion sabrang, areca nuts, and seed of fenugreek. Most people often use a combination therapy of conventional medicine and herbs to achieve the desired therapeutic effect of combination therapy. The use of combination therapy might cause the interaction of pharmacodynamic from those medicines so that it influences the pharmacological effect of one of medicine. The aim of this study is to know the interaction of simvastatin and a cholesterol-lowering herb seen in rats pharmacodynamic simvastatin phase. This research used post-test only controlled group design. Analysis of statistical data normality and homogenity were tested by Kolmogorov Smirnov. The ANOVA test is used when the data is obtained homogeneous but if it is found that the data are not homogeneous then kruskal-wallis test is used. Normal (63.196 mg/dl), negative (70.604 mg/dl), positive (62.512 mg/dl), areca nuts (56.564 mg/dl), fenugreek seed (47.538 ,g/dl), onion sabrang (62.312 mg/dl). The results prove that the combination of herbs and simvastatin did not have a significant difference (P>0,05). The conclusion of this study is that the combination of simvastatin and a cholesterol-lowering herb can cause some pharmacodynamic interactions such as a synergistic effect, antagonist, and a powerful additive, so that combination therapy is not more effective than single simvastatin therapy. The use of the combination therapy is not given in the same time. It would be better if there are some period of time when the combination therapy is applied.

Keywords: onion bulb sabrang, areca nuts, seed of fenugreek, interaction medicine, hyperlipidemia

Procedia PDF Downloads 530

Authors: Claudia Martínez-Araneda, Mariella Gutiérrez, Pedro Gómez, Diego Maldonado, Alejandra Segura, Christian Vidal-Castro

Abstract:

The purpose of this document is to showcase the preliminary work in developing an EduChatbot-type tool and measuring the effects of its use aimed at providing effective feedback to students in programming courses. This bot, hereinafter referred to as tutorBot+, was constructed based on chatGPT and is tasked with assisting and delivering timely positive feedback to students in the field of computer science at the Universidad Católica de Concepción. The proposed working method consists of four stages: (1) Immersion in the domain of Large Language Models (LLMs), (2) Development of the tutorBot+ prototype and integration, (3) Experiment design, and (4) Intervention. The first stage involves a literature review on the use of artificial intelligence in education and the evaluation of intelligent tutors, as well as research on types of feedback for learning and the domain of chatGPT. The second stage encompasses the development of tutorBot+, and the final stage involves a quasi-experimental study with students from the Programming and Database labs, where the learning outcome involves the development of computational thinking skills, enabling the use and measurement of the tool's effects. The preliminary results of this work are promising, as a functional chatBot prototype has been developed in both conversational and non-conversational versions integrated into an open-source online judge and programming contest platform system. There is also an exploration of the possibility of generating a custom model based on a pre-trained one tailored to the domain of programming. This includes the integration of the created tool and the design of the experiment to measure its utility.

Keywords: assessment, chatGPT, learning strategies, LLMs, timely feedback

Procedia PDF Downloads 67

Authors: Ali Muhammad

Abstract:

The study investigates the effect of some determinates of work performance in Kuwaiti business organizations. The study postulates that employee attitudes (organizational commitment, job satisfaction), behaviors (organizational citizenship behavior, job involvement), and emotional intelligence will have positive effects on work performance. Survey data were collected from 204 employees working in eight Kuwaiti work organizations. Data were analyzed using descriptive statistics, Pearson correlation, Cronbach alpha, and regression analysis. Results confirmed the study hypotheses; employee attitudes of organizational commitment and job satisfaction was found to have a significant positive effect on work performance. Organizational citizenship behavior and job involvement were also found to have positive effect on work performance. Findings also revealed that an in increase in emotional intelligent will cause performance to increase. Results of the current study were compared and contrasted to findings of previous studies. The theoretical and empirical application of the findings were explained. Limitation of the current study was discussed and topics for future research were proposed.

Keywords: organizational commitment, Job satisfaction, organizational citizenship behavior, job involvement, emotional intelligence , work performance

Procedia PDF Downloads 193

Authors: Strahinja Kovačević, Jelena Vladić, Senka Vidović, Zoran Zeković, Lidija Jevrić, Sanja Podunavac Kuzmanović

Abstract:

Supercritical extracts of highly valuated medicinal plant Satureja montana were prepared by application of supercritical carbon dioxide extraction in the carbon dioxide pressure range from 125 to 350 bar and temperature range from 40 to 60°C. Using GC/MS method of analysis chemical profiles (aromatic constituents) of S. montana extracts were obtained. Self-training artificial neural networks were applied to predict the retention time of the analyzed terpenes in GC/MS system. The best ANN model obtained was multilayer perceptron (MLP 11-11-1). Hidden activation was tanh and output activation was identity with Broyden–Fletcher–Goldfarb–Shanno training algorithm. Correlation measures of the obtained network were the following: R(training) = 0.9975, R(test) = 0.9971 and R(validation) = 0.9999. The comparison of the experimental and predicted retention times of the analyzed compounds showed very high correlation (R = 0.9913) and significant predictive power of the established neural network.

Keywords: ANN regression, GC/MS, Satureja montana, terpenes

Procedia PDF Downloads 451